Read 3 to Read 2 mapping
A Warwick and CY Ung
16 March, 2022
Last updated: 2022-03-16
Checks: 5 2
Knit directory: codemapper/
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library(tidyverse)
library(reactable)
library(readxl)
library(crosstalk)
library(targets)
library(codemapper)
library(flextable)
all_lkps_maps <- tar_read(all_lkps_maps_raw) %>%
purrr::map(codemapper:::rm_footer_rows_all_lkps_maps_df) %>%
purrr::map(~ tibble::rowid_to_column(.data = .x,
var = ".rowid"))
# choose between `read2_read3` and `read3_read2`
FROM_TO <- "read3_read2"
if (FROM_TO == "read2_read3") {
args <- codemapper:::check_mapping_args(from = "read2",
to = "read3")
from_code <- "Read 2"
} else if (FROM_TO == "read3_read2") {
args <- codemapper:::check_mapping_args(from = "read3",
to = "read2")
from_code <- "Read 3"
}
read_read_map <- all_lkps_maps[[args$mapping_table]]Key points
- Not all mappings are ‘assured’, but most are.
- The
map_codesfunction incodemapperexcludes mappings that are not assured (flagged asIS_ASSURED‘0’) by default.
Preferred terms and synonyms
Read 2 and Read 3 codes have a preferred description and may additionally have synonyms. For example, diabetes (Read 2 C10E.) may be described as either ‘Type 1 diabetes mellitus’ or ‘Insulin dependent diabetes mellitus’.
Overlap between preferred description and synonyms
Read 2:
read2_p <- read_read_map %>%
filter(TERMV2_TYPE == "P")
read2_s <- read_read_map %>%
filter(TERMV2_TYPE == "S")
list(read2_p = unique(read2_p$READV2_CODE),
read2_s = unique(read2_s$READV2_CODE)) %>%
ggVennDiagram::ggVennDiagram()
Read 3:
read3_p <- read_read_map %>%
filter(TERMV3_TYPE == "P")
read3_s <- read_read_map %>%
filter(TERMV3_TYPE == "S")
list(read3_p = unique(read3_p$READV3_CODE),
read3_s = unique(read3_s$READV3_CODE)) %>%
ggVennDiagram::ggVennDiagram()
Not assured mappings
# filter for 'assured' mappings only
read_read_map_is_assured_only <- read_read_map %>%
filter(IS_ASSURED == 1)
# get read codes that are do not have an 'assured' mapping
not_assured_mappings <- read_read_map %>%
filter(!.data[[args$from_col]] %in% read_read_map_is_assured_only[[args$from_col]])
# numbers
n_from_codes_not_assured <- n_distinct(not_assured_mappings[[args$from_col]])
n_from_codes <- n_distinct(read_read_map[[args$from_col]])
pct_from_codes_not_assured <- format(n_from_codes_not_assured/n_from_codes * 100, digits = 3)The current clinical assurance status of each code mapping, where ‘0’ = Not assured and ‘1’ = Assured (column IS_ASSURED). Below is a table of all mappings that are not assured. This includes 183431 out of 330651 Read 3 codes (55.5%).
not_assured_mappings %>%
reactable(filterable = TRUE,
searchable = TRUE,
showPageSizeOptions = TRUE,
pageSizeOptions = c(10, 25, 50, 100, 200),
paginationType = 'jump')